Development, degeneration and neural network of the bodily self

The question addressed by this dissertation is how the human brain builds a coherent representation of the body, and how this representation is used to recognize its own body. Recent approaches by neuroimaging and TMS revealed hints for a distinct brain representation of human body, as compared with other stimulus categories. Neuropsychological studies demonstrated that body-parts and self body-parts recognition are separate processes sub-served by two different, even if possibly overlapping, networks within the brain. Bodily self-recognition is one aspect of our ability to distinguish between self and others and the self/other distinction is a crucial aspect of social behaviour. This is the reason why I have conducted a series of experiment on subjects with everyday difficulties in social and emotional behaviour, such as patients with autism spectrum disorders (ASD) and patients with Parkinson’s disease (PD). More specifically, I studied the implicit self body/face recognition (Chapter 6) and the influence of emotional body postures on bodily self-processing in TD children as well as in ASD children (Chapter 7). I found that the bodily self-recognition is present in TD and in ASD children and that emotional body postures modulate self and others’ body processing. Subsequently, I compared implicit and explicit bodily self-recognition in a neuro-degenerative pathology, such as in PD patients, and I found a selective deficit in implicit but not in explicit self-recognition (Chapter 8). This finding suggests that implicit and explicit bodily self-recognition are separate processes subtended by different mechanisms that can be selectively impaired. If the bodily self is crucial for self/other distinction, the space around the body (personal space) represents the space of interaction and communication with others. When, I studied this space in autism, I found that personal space regulation is impaired in ASD children (Chapter 9).

Novel investigation methods in Computational Social Dynamics and Complex Systems

In this thesis the evolution of the techno-social systems analysis methods will be reported, through the explanation of the various research experience directly faced. The first case presented is a research based on data mining of a dataset of words association named Human Brain Cloud: validation will be faced and, also through a non-trivial modeling, a better understanding of language properties will be presented. Then, a real complex system experiment will be introduced: the WideNoise experiment in the context of the EveryAware european project. The project and the experiment course will be illustrated and data analysis will be displayed. Then the Experimental Tribe platform for social computation will be introduced . It has been conceived to help researchers in the implementation of web experiments, and aims also to catalyze the cumulative growth of experimental methodologies and the standardization of tools cited above. In the last part, three other research experience which already took place on the Experimental Tribe platform will be discussed in detail, from the design of the experiment to the analysis of the results and, eventually, to the modeling of the systems involved. The experiments are: CityRace, about the measurement of human traffic-facing strategies; laPENSOcosì, aiming to unveil the political opinion structure; AirProbe, implemented again in the EveryAware project framework, which consisted in monitoring air quality opinion shift of a community informed about local air pollution. At the end, the evolution of the technosocial systems investigation methods shall emerge together with the opportunities and the threats offered by this new scientific path.

Implementation of a VLSI amplifier interfaced with biomedical sensors for ultra wide band data transmission

This thesis presents a CMOS Amplifier with High Common Mode rejection designed in UMC 130nm technology. The goal is to achieve a high amplification factor for a wide range of biological signals (with frequencies in the range of 10Hz-1KHz) and to reject the common-mode noise signal. It is here presented a Data Acquisition System, composed of a Delta-Sigma-like Modulator and an antenna, that is the core of a portable low-complexity radio system; the amplifier is designed in order to interface the data acquisition system with a sensor that acquires the electrical signal. The Modulator asynchronously acquires and samples human muscle activity, by sending a Quasi-Digital pattern that encodes the acquired signal. There is only a minor loss of information translating the muscle activity using this pattern, compared to an encoding technique which uses astandard digital signal via Impulse-Radio Ultra-Wide Band (IR-UWB). The biological signals, needed for Electromyographic analysis, have an amplitude of 10-100μV and need to be highly amplified and separated from the overwhelming 50mV common mode noise signal. Various tests of the firmness of the concept are presented, as well the proof that the design works even with different sensors, such as Radiation measurement for Dosimetry studies.

Deep learning for computer vision: a comparison between convolutional neural networks and hierarchical temporal memories on object recognition tasks

In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general. However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind of brute-force statistical approaches and if they can only work in the context of High Performance Computing with tons of data. Another important question is whether they are really biologically inspired, as claimed in certain cases, and if they can scale well in terms of "intelligence". The dissertation is focused on trying to answer these key questions in the context of Computer Vision and, in particular, Object Recognition, a task that has been heavily revolutionized by recent advances in the field. Practically speaking, these answers are based on an exhaustive comparison between two, very different, deep learning techniques on the aforementioned task: Convolutional Neural Network (CNN) and Hierarchical Temporal memory (HTM). They stand for two different approaches and points of view within the big hat of deep learning and are the best choices to understand and point out strengths and weaknesses of each of them. CNN is considered one of the most classic and powerful supervised methods used today in machine learning and pattern recognition, especially in object recognition. CNNs are well received and accepted by the scientific community and are already deployed in large corporation like Google and Facebook for solving face recognition and image auto-tagging problems. HTM, on the other hand, is known as a new emerging paradigm and a new meanly-unsupervised method, that is more biologically inspired. It tries to gain more insights from the computational neuroscience community in order to incorporate concepts like time, context and attention during the learning process which are typical of the human brain. In the end, the thesis is supposed to prove that in certain cases, with a lower quantity of data, HTM can outperform CNN.

Stimulus predictability reduces responses in primary visual cortex

In this functional magnetic resonance imaging study we tested whether the predictability of stimuli affects responses in primary visual cortex (V1). The results of this study indicate that visual stimuli evoke smaller responses in V1 when their onset or motion direction can be predicted from the dynamics of surrounding illusory motion. We conclude from this finding that the human brain anticipates forthcoming sensory input that allows predictable visual stimuli to be processed with less neural activation at early stages of cortical processing.

Evolution of genuine cross-correlation strength of focal onset seizures

To quantify the evolution of genuine zero-lag cross-correlations of focal onset seizures, we apply a recently introduced multivariate measure to broad band and to narrow-band EEG data. For frequency components below 12.5 Hz, the strength of genuine cross-correlations decreases significantly during the seizure and the immediate postseizure period, while higher frequency bands show a tendency of elevated cross-correlations during the same period. We conclude that in terms of genuine zero-lag cross-correlations, the electrical brain activity as assessed by scalp electrodes shows a significant spatial fragmentation, which might promote seizure offset.

Quantitative mapping of T2 using partial spoiling

Fast quantitative MRI has become an important tool for biochemical characterization of tissue beyond conventional T1, T2, and T2*-weighted imaging. As a result, steady-state free precession (SSFP) techniques have attracted increased interest, and several methods have been developed for rapid quantification of relaxation times using steady-state free precession. In this work, a new and fast approach for T2 mapping is introduced based on partial RF spoiling of nonbalanced steady-state free precession. The new T2 mapping technique is evaluated and optimized from simulations, and in vivo results are presented for human brain at 1.5 T and for human articular cartilage at 3.0 T. The range of T2 for gray and white matter was from 60 msec (for the corpus callosum) to 100 msec (for cortical gray matter). For cartilage, spatial variation in T2 was observed between deep (34 msec) and superficial (48 msec) layers, as well as between tibial (33 msec), femoral, (54 msec) and patellar (43 msec) cartilage. Excellent correspondence between T2 values derived from partially spoiled SSFP scans and the ones found with a reference multicontrast spin-echo technique is observed, corroborating the accuracy of the new method for proper T2 mapping. Finally, the feasibility of a fast high-resolution quantitative partially spoiled SSFP T2 scan is demonstrated at 7.0 T for human patellar cartilage.

Electrophysiological and behavioral correlates of stable automatic semantic retrieval in aging

Previous studies have shown both declining and stable semantic-memory abilities during healthy aging. There is consistent evidence that semantic processes involving controlled mechanisms weaken with age. In contrast, results of aging studies on automatic semantic retrieval are often inconsistent, probably due to methodological limitations and differences. The present study therefore examines age-related alterations in automatic semantic retrieval and memory structure with a novel combination of critical methodological factors, i.e., the selection of subjects, a well-designed paradigm, and electrophysiological methods that result in unambiguous signal markers. Healthy young and elderly participants performed lexical decisions on visually presented word/non-word pairs with a stimulus onset asynchrony (SOA) of 150 ms. Behavioral and electrophysiological data were measured, and the N400-LPC complex, an event-related potential component sensitive to lexical-semantic retrieval, was analyzed by power and topographic distribution of electrical brain activity. Both age groups exhibited semantic priming (SP) and concreteness effects in behavioral reaction time and the electrophysiological N400-LPC complex. Importantly, elderly subjects did not differ significantly from the young in their lexical decision and SP performances as well as in the N400-LPC SP effect. The only difference was an age-related delay measured in the N400-LPC microstate. This could be attributed to existing age effects in controlled functions, as further supported by the replicated age difference in word fluency. The present results add new behavioral and neurophysiological evidence to earlier findings, by showing that automatic semantic retrieval remains stable in global signal strength and topographic distribution during healthy aging.

Artists' advance: decreased upper alpha power while drawing in artists compared with non-artists

Brain mechanisms associated with artistic talents or skills are still not well understood. This exploratory study investigated differences in brain activity of artists and non-artists while drawing previously presented perspective line-drawings from memory and completing other drawing-related tasks. Electroencephalography (EEG) data were analyzed for power in the frequency domain by means of a Fast Fourier Transform (FFT). Low Resolution Brain Electromagnetic Tomography (LORETA) was applied to localize emerging significances. During drawing and related tasks, decreased power was seen in artists compared to non-artists mainly in upper alpha frequency ranges. Decreased alpha power is often associated with an increase in cognitive functioning and may reflect enhanced semantic memory performance and object recognition processes in artists. These assumptions are supported by the behavioral data assessed in this study and complement previous findings showing increased parietal activations in non-artists compared to artists while drawing. However, due to the exploratory nature of the analysis, additional confirmatory studies will be needed.

On seeing the trees and the forest: Single-signal and multisignal analysis of periictal intracranial EEG

Epileptic seizures are associated with a dysregulation of electrical brain activity on many different spatial scales. To better understand the dynamics of epileptic seizures, that is, how the seizures initiate, propagate, and terminate, it is important to consider changes of electrical brain activity on different spatial scales. Herein we set out to analyze periictal electrical brain activity on comparatively small and large spatial scales by assessing changes in single intracranial electroencephalography (EEG) signals and of averaged interdependences of pairs of EEG signals.

LGI2 truncation causes a remitting focal epilepsy in dogs

One quadrillion synapses are laid in the first two years of postnatal construction of the human brain, which are then pruned until age 10 to 500 trillion synapses composing the final network. Genetic epilepsies are the most common neurological diseases with onset during pruning, affecting 0.5% of 2-10-year-old children, and these epilepsies are often characterized by spontaneous remission. We previously described a remitting epilepsy in the Lagotto romagnolo canine breed. Here, we identify the gene defect and affected neurochemical pathway. We reconstructed a large Lagotto pedigree of around 34 affected animals. Using genome-wide association in 11 discordant sib-pairs from this pedigree, we mapped the disease locus to a 1.7 Mb region of homozygosity in chromosome 3 where we identified a protein-truncating mutation in the Lgi2 gene, a homologue of the human epilepsy gene LGI1. We show that LGI2, like LGI1, is neuronally secreted and acts on metalloproteinase-lacking members of the ADAM family of neuronal receptors, which function in synapse remodeling, and that LGI2 truncation, like LGI1 truncations, prevents secretion and ADAM interaction. The resulting epilepsy onsets at around seven weeks (equivalent to human two years), and remits by four months (human eight years), versus onset after age eight in the majority of human patients with LGI1 mutations. Finally, we show that Lgi2 is expressed highly in the immediate post-natal period until halfway through pruning, unlike Lgi1, which is expressed in the latter part of pruning and beyond. LGI2 acts at least in part through the same ADAM receptors as LGI1, but earlier, ensuring electrical stability (absence of epilepsy) during pruning years, preceding this same function performed by LGI1 in later years. LGI2 should be considered a candidate gene for common remitting childhood epilepsies, and LGI2-to-LGI1 transition for mechanisms of childhood epilepsy remission.

Subcortical electrostimulation to identify network subserving motor control

Objectives: Recent anatomical-functional studies have transformed our understanding of cerebral motor control away from a hierarchical structure and toward parallel and interconnected specialized circuits. Subcortical electrical stimulation during awake surgery provides a unique opportunity to identify white matter tracts involved in motor control. For the first time, this study reports the findings on motor modulatory responses evoked by subcortical stimulation and investigates the cortico-subcortical connectivity of cerebral motor control. Experimental design: Twenty-one selected patients were operated while awake for frontal, insular, and parietal diffuse low-grade gliomas. Subcortical electrostimulation mapping was used to search for interference with voluntary movements. The corresponding stimulation sites were localized on brain schemas using the anterior and posterior commissures method. Principal observations: Subcortical negative motor responses were evoked in 20/21 patients, whereas acceleration of voluntary movements and positive motor responses were observed in three and five patients, respectively. The majority of the stimulation sites were detected rostral of the corticospinal tract near the vertical anterior-commissural line, and additional sites were seen in the frontal and parietal white matter. Conclusions: The diverse interferences with motor function resulting in inhibition and acceleration imply a modulatory influence of the detected fiber network. The subcortical stimulation sites were distributed veil-like, anterior to the primary motor fibers, suggesting descending pathways originating from premotor areas known for negative motor response characteristics. Further stimulation sites in the parietal white matter as well as in the anterior arm of the internal capsule indicate a large-scale fronto-parietal motor control network. Hum Brain Mapp, 2012. © 2012 Wiley Periodicals, Inc.

Marked global reduction in mGluR5 receptor binding in smokers and ex-smokers determined by [11C]ABP688 positron emission tomography

Nicotine addiction is a major public health problem, resulting in primary glutamatergic dysfunction. We measured the glutamate receptor binding in the human brain and provided direct evidence for the abnormal glutamate system in smokers. Because antagonism of the metabotropic glutamate receptor 5 (mGluR5) reduced nicotine self-administration in rats and mice, mGluR5 is suggested to be involved in nicotine addiction. mGluR5 receptor binding specifically to an allosteric site was observed by using positron emission tomography with [(11)C]ABP688. We found a marked global reduction (20.6%; P < 0.0001) in the mGluR5 distribution volume ratio (DVR) in the gray matter of 14 smokers. The most prominent reductions were found in the bilateral medial orbitofrontal cortex. Compared with 14 nonsmokers, 14 ex-smokers had global reductions in the average gray matter mGluR5 DVR (11.5%; P < 0.005), and there was a significant difference in average gray matter mGluR5 DVR between smokers and ex-smokers (9.2%; P < 0.01). Clinical variables reflecting current nicotine consumption, dependence and abstinence were not correlated with mGluR5 DVR. This decrease in mGluR5 receptor binding may be an adaptation to chronic increases in glutamate induced by chronic nicotine administration, and the decreased down-regulation seen in the ex-smokers could be due to incomplete recovery of the receptors, especially because the ex-smokers were abstinent for only 25 wk on average. These results encourage the development and testing of drugs against addiction that directly target the glutamatergic system.

Auditory motion direction encoding in auditory cortex and high-level visual cortex

The aim of this functional magnetic resonance imaging (fMRI) study was to identify human brain areas that are sensitive to the direction of auditory motion. Such directional sensitivity was assessed in a hypothesis-free manner by analyzing fMRI response patterns across the entire brain volume using a spherical-searchlight approach. In addition, we assessed directional sensitivity in three predefined brain areas that have been associated with auditory motion perception in previous neuroimaging studies. These were the primary auditory cortex, the planum temporale and the visual motion complex (hMT/V5+). Our whole-brain analysis revealed that the direction of sound-source movement could be decoded from fMRI response patterns in the right auditory cortex and in a high-level visual area located in the right lateral occipital cortex. Our region-of-interest-based analysis showed that the decoding of the direction of auditory motion was most reliable with activation patterns of the left and right planum temporale. Auditory motion direction could not be decoded from activation patterns in hMT/V5+. These findings provide further evidence for the planum temporale playing a central role in supporting auditory motion perception. In addition, our findings suggest a cross-modal transfer of directional information to high-level visual cortex in healthy humans.